| .. _cloudxr-teleoperation-cluster: | |
| Deploying CloudXR Teleoperation on Kubernetes | |
| ============================================= | |
| .. currentmodule:: isaaclab | |
| This section explains how to deploy CloudXR Teleoperation for Isaac Lab on a Kubernetes (K8s) cluster. | |
| .. _k8s-system-requirements: | |
| System Requirements | |
| ------------------- | |
| * **Minimum requirement**: Kubernetes cluster with a node that has at least 1 NVIDIA RTX PRO 6000 / L40 GPU or equivalent | |
| * **Recommended requirement**: Kubernetes cluster with a node that has at least 2 RTX PRO 6000 / L40 GPUs or equivalent | |
| .. note:: | |
| If you are using DGX Spark, check `DGX Spark Limitations <https://isaac-sim.github.io/IsaacLab/release/2.3.0/source/setup/installation/index.html#dgx-spark-details-and-limitations>`_ for compatibility. | |
| Software Dependencies | |
| --------------------- | |
| * ``kubectl`` on your host computer | |
| * If you use MicroK8s, you already have ``microk8s kubectl`` | |
| * Otherwise follow the `official kubectl installation guide <https://kubernetes.io/docs/tasks/tools/#kubectl>`_ | |
| * ``helm`` on your host computer | |
| * If you use MicroK8s, you already have ``microk8s helm`` | |
| * Otherwise follow the `official Helm installation guide <https://helm.sh/docs/intro/install/>`_ | |
| * Access to NGC public registry from your Kubernetes cluster, in particular these container images: | |
| * ``https://catalog.ngc.nvidia.com/orgs/nvidia/containers/isaac-lab`` | |
| * ``https://catalog.ngc.nvidia.com/orgs/nvidia/containers/cloudxr-runtime`` | |
| * NVIDIA GPU Operator or equivalent installed in your Kubernetes cluster to expose NVIDIA GPUs | |
| * NVIDIA Container Toolkit installed on the nodes of your Kubernetes cluster | |
| Preparation | |
| ----------- | |
| On your host computer, you should have already configured ``kubectl`` to access your Kubernetes cluster. To validate, run the following command and verify it returns your nodes correctly: | |
| .. code:: bash | |
| kubectl get node | |
| If you are installing this to your own Kubernetes cluster instead of using the setup described in the :ref:`k8s-appendix`, your role in the K8s cluster should have at least the following RBAC permissions: | |
| .. code:: yaml | |
| rules: | |
| - apiGroups: [""] | |
| resources: ["configmaps"] | |
| verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] | |
| - apiGroups: ["apps"] | |
| resources: ["deployments", "replicasets"] | |
| verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] | |
| - apiGroups: [""] | |
| resources: ["pods"] | |
| verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] | |
| - apiGroups: [""] | |
| resources: ["services"] | |
| verbs: ["get", "list", "watch", "create", "update", "patch", "delete"] | |
| .. _k8s-installation: | |
| Installation | |
| ------------ | |
| .. note:: | |
| The following steps are verified on a MicroK8s cluster with GPU Operator installed (see configurations in the :ref:`k8s-appendix`). You can configure your own K8s cluster accordingly if you encounter issues. | |
| #. Download the Helm chart from NGC (get your NGC API key based on the `public guide <https://docs.nvidia.com/ngc/ngc-overview/index.html#generating-api-key>`_): | |
| .. code:: bash | |
| helm fetch https://helm.ngc.nvidia.com/nvidia/charts/isaac-lab-teleop-2.3.0.tgz \ | |
| --username='$oauthtoken' \ | |
| --password=<your-ngc-api-key> | |
| #. Install and run the CloudXR Teleoperation for Isaac Lab pod in the default namespace, consuming all host GPUs: | |
| .. code:: bash | |
| helm upgrade --install hello-isaac-teleop isaac-lab-teleop-2.3.0.tgz \ | |
| --set fullnameOverride=hello-isaac-teleop \ | |
| --set hostNetwork="true" | |
| .. note:: | |
| You can remove the need for host network by creating an external LoadBalancer VIP (e.g., with MetalLB), and setting the environment variable ``NV_CXR_ENDPOINT_IP`` when deploying the Helm chart: | |
| .. code:: yaml | |
| # local_values.yml file example: | |
| fullnameOverride: hello-isaac-teleop | |
| streamer: | |
| extraEnvs: | |
| - name: NV_CXR_ENDPOINT_IP | |
| value: "<your external LoadBalancer VIP>" | |
| - name: ACCEPT_EULA | |
| value: "Y" | |
| .. code:: bash | |
| # command | |
| helm upgrade --install --values local_values.yml \ | |
| hello-isaac-teleop isaac-lab-teleop-2.3.0.tgz | |
| #. Verify the deployment is completed: | |
| .. code:: bash | |
| kubectl wait --for=condition=available --timeout=300s \ | |
| deployment/hello-isaac-teleop | |
| After the pod is running, it might take approximately 5-8 minutes to complete loading assets and start streaming. | |
| Uninstallation | |
| -------------- | |
| You can uninstall by simply running: | |
| .. code:: bash | |
| helm uninstall hello-isaac-teleop | |
| .. _k8s-appendix: | |
| Appendix: Setting Up a Local K8s Cluster with MicroK8s | |
| ------------------------------------------------------ | |
| Your local workstation should have the NVIDIA Container Toolkit and its dependencies installed. Otherwise, the following setup will not work. | |
| Cleaning Up Existing Installations (Optional) | |
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |
| .. code:: bash | |
| # Clean up the system to ensure we start fresh | |
| sudo snap remove microk8s | |
| sudo snap remove helm | |
| sudo apt-get remove docker-ce docker-ce-cli containerd.io | |
| # If you have snap docker installed, remove it as well | |
| sudo snap remove docker | |
| Installing MicroK8s | |
| ~~~~~~~~~~~~~~~~~~~ | |
| .. code:: bash | |
| sudo snap install microk8s --classic | |
| Installing NVIDIA GPU Operator | |
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |
| .. code:: bash | |
| microk8s helm repo add nvidia https://helm.ngc.nvidia.com/nvidia | |
| microk8s helm repo update | |
| microk8s helm install gpu-operator \ | |
| -n gpu-operator \ | |
| --create-namespace nvidia/gpu-operator \ | |
| --set toolkit.env[0].name=CONTAINERD_CONFIG \ | |
| --set toolkit.env[0].value=/var/snap/microk8s/current/args/containerd-template.toml \ | |
| --set toolkit.env[1].name=CONTAINERD_SOCKET \ | |
| --set toolkit.env[1].value=/var/snap/microk8s/common/run/containerd.sock \ | |
| --set toolkit.env[2].name=CONTAINERD_RUNTIME_CLASS \ | |
| --set toolkit.env[2].value=nvidia \ | |
| --set toolkit.env[3].name=CONTAINERD_SET_AS_DEFAULT \ | |
| --set-string toolkit.env[3].value=true | |
| .. note:: | |
| If you have configured the GPU operator to use volume mounts for ``DEVICE_LIST_STRATEGY`` on the device plugin and disabled ``ACCEPT_NVIDIA_VISIBLE_DEVICES_ENVVAR_WHEN_UNPRIVILEGED`` on the toolkit, this configuration is currently unsupported, as there is no method to ensure the assigned GPU resource is consistently shared between containers of the same pod. | |
| Verifying Installation | |
| ~~~~~~~~~~~~~~~~~~~~~~ | |
| Run the following command to verify that all pods are running correctly: | |
| .. code:: bash | |
| microk8s kubectl get pods -n gpu-operator | |
| You should see output similar to: | |
| .. code:: text | |
| NAMESPACE NAME READY STATUS RESTARTS AGE | |
| gpu-operator gpu-operator-node-feature-discovery-gc-76dc6664b8-npkdg 1/1 Running 0 77m | |
| gpu-operator gpu-operator-node-feature-discovery-master-7d6b448f6d-76fqj 1/1 Running 0 77m | |
| gpu-operator gpu-operator-node-feature-discovery-worker-8wr4n 1/1 Running 0 77m | |
| gpu-operator gpu-operator-86656466d6-wjqf4 1/1 Running 0 77m | |
| gpu-operator nvidia-container-toolkit-daemonset-qffh6 1/1 Running 0 77m | |
| gpu-operator nvidia-dcgm-exporter-vcxsf 1/1 Running 0 77m | |
| gpu-operator nvidia-cuda-validator-x9qn4 0/1 Completed 0 76m | |
| gpu-operator nvidia-device-plugin-daemonset-t4j4k 1/1 Running 0 77m | |
| gpu-operator gpu-feature-discovery-8dms9 1/1 Running 0 77m | |
| gpu-operator nvidia-operator-validator-gjs9m 1/1 Running 0 77m | |
| Once all pods are running, you can proceed to the :ref:`k8s-installation` section. | |